Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update README.md #27

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Update README.md #27

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
18 changes: 16 additions & 2 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -39,18 +39,24 @@ Download [model weights](https://huggingface.co/spaces/Audio-AGI/AudioSep/tree/m
from pipeline import build_audiosep, inference
import torch

# Check if a CUDA-compatible GPU is available, and set the device accordingly
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Build an audio separation model by providing a configuration YAML file, a checkpoint path,
and the chosen device
model = build_audiosep(
config_yaml='config/audiosep_base.yaml',
checkpoint_path='checkpoint/audiosep_base_4M_steps.ckpt',
device=device)

# Define the paths and information for audio separation
audio_file = 'path_to_audio_file'
text = 'textual_description'
output_file='separated_audio.wav'

# AudioSep processes the audio at 32 kHz sampling rate
# Note: The 'inference' function processes audio at a 32 kHz sampling rate
# Perform audio separation using the 'inference' function with the specified model, audio
file, text, output file, and device
inference(model, audio_file, text, output_file, device)
```

Expand All @@ -63,17 +69,25 @@ To load directly from Hugging Face, you can do the following:
from utils import get_ss_model
import torch

# Check if a CUDA-compatible GPU is available and set the device accordingly
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Get the source separation model configuration from the specified YAML file
ss_model = get_ss_model('config/audiosep_base.yaml')

# Create an instance of the AudioSep model from a pretrained model checkpoint,
# while providing the source separation model and setting the device
model = AudioSep.from_pretrained("nielsr/audiosep-demo", ss_model=ss_model)

# Define the paths and information for audio separation
audio_file = 'path_to_audio_file'
text = 'textual_description'
output_file='separated_audio.wav'

# AudioSep processes the audio at 32 kHz sampling rate

# Note: The 'inference' function processes audio at a 32 kHz sampling rate
# Perform audio separation using the 'inference' function with the specified model, audio
file, text, output file, and device
inference(model, audio_file, text, output_file, device)
```
<hr>
Expand Down